


"""simple docstring"""
from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFTransfoXLForSequenceClassification,
     TFTransfoXLLMHeadModel,
     TFTransfoXLModel,
 )




class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   )  ->  Dict:
      _lowerCamelCase	:					Union[str, Any]   =		parent
      _lowerCamelCase	:					str   =		13
      _lowerCamelCase	:					Union[str, Any]   =		7
      _lowerCamelCase	:					Optional[int]   =		30
      _lowerCamelCase	:					Optional[int]   =		self.seq_length + self.mem_len
      _lowerCamelCase	:					Dict   =		15
      _lowerCamelCase	:					int   =		True
      _lowerCamelCase	:					Union[str, Any]   =		True
      _lowerCamelCase	:					List[Any]   =		99
      _lowerCamelCase	:					Tuple   =		[10, 50, 80]
      _lowerCamelCase	:					Tuple   =		32
      _lowerCamelCase	:					int   =		32
      _lowerCamelCase	:					Optional[Any]   =		4
      _lowerCamelCase	:					Optional[Any]   =		8
      _lowerCamelCase	:					List[Any]   =		128
      _lowerCamelCase	:					Dict   =		2
      _lowerCamelCase	:					str   =		2
      _lowerCamelCase	:					Any   =		None
      _lowerCamelCase	:					Union[str, Any]   =		1
      _lowerCamelCase	:					int   =		0
      _lowerCamelCase	:					Tuple   =		3
      _lowerCamelCase	:					str   =		self.vocab_size - 1
      _lowerCamelCase	:					str   =		0.01





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Any   =		ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size)
      _lowerCamelCase	:					Union[str, Any]   =		ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size)

      _lowerCamelCase	:					Dict   =		None
      if self.use_labels:
         _lowerCamelCase	:					Tuple   =		ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size)

      _lowerCamelCase	:					Optional[Any]   =		TransfoXLConfig(
          vocab_size=self.vocab_size      ,   mem_len=self.mem_len      ,   clamp_len=self.clamp_len      ,   cutoffs=self.cutoffs      ,   d_model=self.hidden_size      ,   d_embed=self.d_embed      ,   n_head=self.num_attention_heads      ,   d_head=self.d_head      ,   d_inner=self.d_inner      ,   div_val=self.div_val      ,   n_layer=self.num_hidden_layers      ,   eos_token_id=self.eos_token_id      ,   pad_token_id=self.vocab_size - 1      ,   init_range=self.init_range      ,   num_labels=self.num_labels      ,   )

      return (config, input_ids_a, input_ids_a, lm_labels)





   def 		UpperCamelCase_  (							self)  ->  int:
      random.seed(self.seed)
      tf.random.set_seed(self.seed)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Any:
      _lowerCamelCase	:					str   =		TFTransfoXLModel(SCREAMING_SNAKE_CASE)

      _lowerCamelCase   ,    _lowerCamelCase	:					Union[str, Any]   =		model(SCREAMING_SNAKE_CASE).to_tuple()

      _lowerCamelCase	:					int   =		{"""input_ids""": input_ids_a, """mems""": mems_a}

      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		model(SCREAMING_SNAKE_CASE).to_tuple()

      self.parent.assertEqual(hidden_states_a.shape      ,   (self.batch_size, self.seq_length, self.hidden_size))
      self.parent.assertEqual(hidden_states_a.shape      ,   (self.batch_size, self.seq_length, self.hidden_size))
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]      ,   [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,   )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]      ,   [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,   )





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Tuple:
      _lowerCamelCase	:					str   =		TFTransfoXLLMHeadModel(SCREAMING_SNAKE_CASE)

      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		model(SCREAMING_SNAKE_CASE).to_tuple()

      _lowerCamelCase	:					Tuple   =		{"""input_ids""": input_ids_a, """labels""": lm_labels}
      _lowerCamelCase   ,    _lowerCamelCase	:					Tuple   =		model(SCREAMING_SNAKE_CASE).to_tuple()

      _lowerCamelCase   ,    _lowerCamelCase	:					int   =		model([input_ids_a, mems_a]).to_tuple()

      _lowerCamelCase	:					Optional[Any]   =		{"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[int]   =		model(SCREAMING_SNAKE_CASE).to_tuple()

      self.parent.assertEqual(lm_logits_a.shape      ,   (self.batch_size, self.seq_length, self.vocab_size))
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]      ,   [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,   )

      self.parent.assertEqual(lm_logits_a.shape      ,   (self.batch_size, self.seq_length, self.vocab_size))
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]      ,   [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,   )





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					Tuple   =		TFTransfoXLForSequenceClassification(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Any   =		model(SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_labels))





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					str   =		self.prepare_config_and_inputs()
      ((_lowerCamelCase)   ,    (_lowerCamelCase)   ,    (_lowerCamelCase)   ,    (_lowerCamelCase))	:					int   =		config_and_inputs
      _lowerCamelCase	:					Optional[Any]   =		{"""input_ids""": input_ids_a}
      return config, inputs_dict







@require_tf
class 	lowercase__				(					A_ ,A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					(
       (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
   )
   __UpperCAmelCase							       =					() if is_tf_available() else ()
   __UpperCAmelCase							       =					(
       {
           '''feature-extraction''': TFTransfoXLModel,
           '''text-classification''': TFTransfoXLForSequenceClassification,
           '''text-generation''': TFTransfoXLLMHeadModel,
           '''zero-shot''': TFTransfoXLForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Optional[Any]:
      if pipeline_test_casse_name == "TextGenerationPipelineTests":
         # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
         # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
         # tokenizer.
         return True

      return False





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					List[str]   =		TFTransfoXLModelTester(self)
      _lowerCamelCase	:					Any   =		ConfigTester(self      ,   config_class=SCREAMING_SNAKE_CASE      ,   d_embed=37)





   def 		UpperCamelCase_  (							self)  ->  Any:
      self.config_tester.run_common_tests()





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      self.model_tester.set_seed()
      _lowerCamelCase	:					Union[str, Any]   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_model(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      self.model_tester.set_seed()
      _lowerCamelCase	:					Tuple   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_lm_head(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					str   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase   ,    _lowerCamelCase	:					int   =		self.model_tester.prepare_config_and_inputs_for_common()
      _lowerCamelCase	:					Any   =		[TFTransfoXLForSequenceClassification]

      for model_class in self.all_model_classes:
         _lowerCamelCase	:					str   =		model_class(SCREAMING_SNAKE_CASE)
         assert isinstance(model.get_input_embeddings()      ,   tf.keras.layers.Layer)
         if model_class in list_other_models_with_output_ebd:
            _lowerCamelCase	:					Union[str, Any]   =		model.get_output_embeddings()
            assert isinstance(SCREAMING_SNAKE_CASE      ,   tf.keras.layers.Layer)
            _lowerCamelCase	:					Tuple   =		model.get_bias()
            assert name is None
         else:
            _lowerCamelCase	:					Dict   =		model.get_output_embeddings()
            assert x is None
            _lowerCamelCase	:					List[str]   =		model.get_bias()
            assert name is None





   def 		UpperCamelCase_  (							self)  ->  Dict:
      # TODO JP: Make TransfoXL XLA compliant
      pass





   @slow
   def 		UpperCamelCase_  (							self)  ->  int:
      for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCamelCase	:					Any   =		TFTransfoXLModel.from_pretrained(SCREAMING_SNAKE_CASE)
         self.assertIsNotNone(SCREAMING_SNAKE_CASE)





   @unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss.""")
   def 		UpperCamelCase_  (							self)  ->  Any:
      pass







@require_tf
class 	lowercase__				(					unittest.TestCase				):





   @unittest.skip("""Skip test until #12651 is resolved.""")
   @slow
   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					Optional[int]   =		TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103""")
      # fmt: off
      _lowerCamelCase	:					int   =		tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]]      ,   dtype=tf.intaa)  # noqa: E231
      # fmt: on
      #  In 1991 , the remains of Russian Tsar Nicholas II and his family
      #  ( except for Alexei and Maria ) are discovered .
      #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
      #  remainder of the story . 1883 Western Siberia ,
      #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
      #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
      #  father initially slaps him for making such an accusation , Rasputin watches as the
      #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
      #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
      #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

      # fmt: off
      _lowerCamelCase	:					List[str]   =		[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # noqa: E231
      # fmt: on
      #  In 1991, the remains of Russian Tsar Nicholas II and his family (
      #  except for Alexei and Maria ) are discovered. The voice of young son,
      #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
      #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
      #  and a group of men to perform magic. Rasputin has a vision and
      #  denounces one of the men as a horse thief. Although his father initially
      #  slaps him for making such an accusation, Rasputin watches as the man
      #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
      #  of the Virgin Mary, prompting him to become a priest.
      #  Rasputin quickly becomes famous, with people, even a bishop, begging for
      #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
      # Nicholas II and his family were discovered. The voice of <unk> young son,
      # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

      _lowerCamelCase	:					Optional[int]   =		model.generate(SCREAMING_SNAKE_CASE      ,   max_length=200      ,   do_sample=SCREAMING_SNAKE_CASE)
      self.assertListEqual(output_ids[0].numpy().tolist()      ,   SCREAMING_SNAKE_CASE)



import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD




class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						True
  a__       			=						False
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=UpperCamelCase__     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.out_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.out_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				only_cross_attention=self.only_cross_attention     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions

    if self.add_downsample:
      __magic_name__								=			FlaxDownsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					List[Any]     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Optional[int]=True       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			()

    for resnet, attn in zip(self.resnets     ,				self.attentions       ):
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      output_states += (hidden_states,)

    if self.add_downsample:
      __magic_name__								=			self.downsamplers_a(UpperCamelCase__       )
      output_states += (hidden_states,)

    return hidden_states, output_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						True
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					str       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=UpperCamelCase__     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )
    __magic_name__								=			resnets

    if self.add_downsample:
      __magic_name__								=			FlaxDownsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					str     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					int=True       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			()

    for resnet in self.resnets:
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      output_states += (hidden_states,)

    if self.add_downsample:
      __magic_name__								=			self.downsamplers_a(UpperCamelCase__       )
      output_states += (hidden_states,)

    return hidden_states, output_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						True
  a__       			=						False
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if (i == self.num_layers - 1) else self.out_channels
      __magic_name__								=			self.prev_output_channel if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=resnet_in_channels + res_skip_channels     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.out_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.out_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				only_cross_attention=self.only_cross_attention     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions

    if self.add_upsample:
      __magic_name__								=			FlaxUpsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					Dict=True       )     ->      Union[str, Any]:







    """simple docstring"""






    for resnet, attn in zip(self.resnets     ,				self.attentions       ):
      # pop res hidden states
      __magic_name__								=			res_hidden_states_tuple[-1]
      __magic_name__								=			res_hidden_states_tuple[:-1]
      __magic_name__								=			jnp.concatenate((hidden_states, res_hidden_states)     ,				axis=-1       )

      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    if self.add_upsample:
      __magic_name__								=			self.upsamplers_a(UpperCamelCase__       )

    return hidden_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						True
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					List[Any]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if (i == self.num_layers - 1) else self.out_channels
      __magic_name__								=			self.prev_output_channel if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=resnet_in_channels + res_skip_channels     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

    __magic_name__								=			resnets

    if self.add_upsample:
      __magic_name__								=			FlaxUpsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					List[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					str=True       )     ->      Dict:







    """simple docstring"""






    for resnet in self.resnets:
      # pop res hidden states
      __magic_name__								=			res_hidden_states_tuple[-1]
      __magic_name__								=			res_hidden_states_tuple[:-1]
      __magic_name__								=			jnp.concatenate((hidden_states, res_hidden_states)     ,				axis=-1       )

      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    if self.add_upsample:
      __magic_name__								=			self.upsamplers_a(UpperCamelCase__       )

    return hidden_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					List[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[
        FlaxResnetBlockaD(
            in_channels=self.in_channels     ,				out_channels=self.in_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
    ]

    __magic_name__								=			[]

    for _ in range(self.num_layers       ):
      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.in_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.in_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=self.in_channels     ,				out_channels=self.in_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions





  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					int=True       )     ->      str:







    """simple docstring"""






    __magic_name__								=			self.resnets[0](UpperCamelCase__     ,				UpperCamelCase__       )
    for attn, resnet in zip(self.attentions     ,				self.resnets[1:]       ):
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    return hidden_states

